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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Surface Heterogeneity-Involved Estimation of Sample Size for Accuracy Assessment of Land Cover Product from Satellite Imagery
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Surface Heterogeneity-Involved Estimation of Sample Size for Accuracy Assessment of Land Cover Product from Satellite Imagery

机译：地表非均质性估计的样本量用于卫星图像土地覆盖产品的准确性评估

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Sample size estimation is a key issue for validating land cover products derived from satellite images. Based on the fact that present sample size estimation methods account for the characteristics of the Earth’s subsurface, this study developed a model for estimating sample size by considering the scale effect and surface heterogeneity. First, we introduced a watershed with different areas to indicate the scale effect on the sample size. Then, by employing an all-subsets regression feature selection method, three landscape indicators describing the aggregation and diversity of the land cover patches were selected (from 14 indicators) as the main factors for indicating the surface heterogeneity. Finally, we developed a multi-level linear model for sample size estimation using explanatory variables, including the estimated sample size ( ) calculated from the traditional statistical model, size of the test region, and three landscape indicators. As reference data for developing this model, we employed a case study in the Jiangxi Province using a 30 m spatial resolution global land cover product (Globeland30) from 2010 as a classified map, and national 30 m land use/cover change (LUCC) data from 2010 in China. The results showed that the adjusted square coefficient of R is 0.79, indicating that the joint explanatory ability of all predictive variables in the model to the sample size is 79%. This means that the predictability of this model is at a good level. By comparing the sample size obtained by the developed multi-level linear model and as calculated from the statistics model, we find that is much smaller than , which mainly contributes to the concerns regarding surface heterogeneity in this study. The validity of the established model is tested and is proven as effective in the Anhui Province. This indicates that the estimated sample size from considering the scale effect and spatial heterogeneity in this study achieved the same accuracy as that calculated from a probability statistical model, while simultaneously saving more time, labour, and money in the accuracy assessment of a land cover dataset.
机译：样本量估计是验证源自卫星图像的土地覆盖产品的关键问题。基于当前样本量估算方法考虑到了地球地下特征的事实，本研究开发了一种模型，该模型通过考虑尺度效应和表面异质性来估算样本量。首先，我们引入了一个具有不同区域的分水岭，以表明尺度对样本量的影响。然后，通过采用全子集回归特征选择方法，从14个指标中选择了3个描述土地覆盖斑块的聚集和多样性的景观指标作为指示表面异质性的主要因素。最后，我们使用解释变量开发了用于样本量估计的多级线性模型，包括从传统统计模型计算出的估计样本量（），测试区域的大小和三个景观指标。作为开发此模型的参考数据，我们在江西省进行了案例研究，使用了2010年以来30 m的空间分辨率全球土地覆盖产品（Globeland30）作为分类地图，以及全国30 m的土地利用/覆盖变化（LUCC）数据从2010年在中国开始。结果表明，调整后的R的平方系数为0.79，表明模型中所有预测变量对样本大小的联合解释能力为79％。这意味着该模型的可预测性处于良好水平。通过比较由开发的多级线性模型和统计模型计算得出的样本量，我们发现该样本量远小于，这主要是造成本研究中有关表面异质性的担忧。测试了所建立模型的有效性，并在安徽省证明是有效的。这表明，在这项研究中，通过考虑尺度效应和空间异质性而估算出的样本量达到了与概率统计模型所计算出的相同的准确性，同时在土地覆盖数据集的准确性评估中节省了更多的时间，劳动力和金钱。 。

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